{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de6a61f0-9931-4da5-b032-6fa5e73d6907",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 步骤1：数据提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "831df832-d26f-45ca-8e06-945549b4ae50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>25.740</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>3756.62160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520\n",
       "5   31  female  25.740         0     no  southeast   3756.62160"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "data=pd.read_csv('./data/insurance.csv',sep=',') # 按Tab键补齐   读csv的路径，sep指明文件数据的分隔符  \n",
    "data.head(n=6) # 取出前6行看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "05ac832e-8d7c-4863-b5f4-a650ec8707ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 单行来看，前6个数据就是特征x,而最后的花费charges就是y 是有监督学习\n",
    "# y是连续的浮点数，要做回归任务,而不是分类 \n",
    "# 目标为预测出y=f(x)中的f\n",
    "# 特征x需要对y有影响，比如姓名，用户ID和花费y没任何关系，不需要处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93464366-26e4-4670-bc31-0ffc060d10f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f0d97eb-31fe-4523-b96d-28d1aef12e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 步骤2：EDA数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de5b3737-bf9f-4b38-b2f5-9c9d7122b967",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdd358f3-95c4-4f7d-a365-76cc6eebca1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "调整后数据\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data['charges']) # 画y的柱状图\n",
    "plt.show()\n",
    "# 线性回归时，要求数据y服从正态分布，才能有损失MSE\n",
    "# 原始数据数学上叫右偏，需要调整成正态分布\n",
    "print('调整后数据')\n",
    "plt.hist(np.log(data['charges']),bins=20) # 画y的柱状图  bins控制图粗细\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68450a62-41af-44cf-88f6-c7f064967e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.kdeplot(data.loc[data.sex=='male', 'charges'], fill=True, label='male') #选择sex维度画核密度图，直观分析数据\n",
    "sns.kdeplot(data.loc[data.sex=='female', 'charges'], fill=True, label='female')\n",
    "plt.legend() #显示调用图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2bd55158-64ad-4ca4-a8b6-b30a56fb9502",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sex特征区别不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4debba9e-842a-47f4-ad75-b7b5d9705f56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data.loc[data.region=='northwest', 'charges'], fill=True, label='northwest')\n",
    "sns.kdeplot(data.loc[data.region=='southwest', 'charges'], fill=True, label='southwest')\n",
    "sns.kdeplot(data.loc[data.region=='northeast', 'charges'], fill=True, label='northeast')\n",
    "sns.kdeplot(data.loc[data.region=='southeast', 'charges'], fill=True, label='southeast')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28d1aa11-e713-43e5-bccd-2e29d8e08a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# region特征区别不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "705455e7-c8f9-4b17-921e-7822589b6d55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data.loc[data.smoker=='yes', 'charges'], fill=True, label='smoker yes')\n",
    "sns.kdeplot(data.loc[data.smoker=='no', 'charges'], fill=True, label='smoker no')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0fc7181d-673d-4ce1-806b-442e229e194d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# smoker特征区别很大 吸烟者普遍花销很大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e5680803-ea90-4ca0-a84c-f072eb23ceb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data.loc[data.children==0, 'charges'], fill=True, label='children 0')\n",
    "sns.kdeplot(data.loc[data.children==1, 'charges'], fill=True, label='children 1')\n",
    "sns.kdeplot(data.loc[data.children==2, 'charges'], fill=True, label='children 2')\n",
    "sns.kdeplot(data.loc[data.children==3, 'charges'], fill=True, label='children 3')\n",
    "sns.kdeplot(data.loc[data.children==4, 'charges'], fill=True, label='children 4')\n",
    "sns.kdeplot(data.loc[data.children==5, 'charges'], fill=True, label='children 5')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52e5a102-7642-4448-bd45-b5abd20b27dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 孩子数为0时，密度高点的花费相对是最低的，孩子数非0的密度最高点的花费相差不大\n",
    "# EDA服务于特征工程，可以进行降噪，特征选择，去掉不重要特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ef3d92c-10ac-4e0f-81ce-3ba94c405b86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da828e75-fa67-47c5-be5c-55dec2ccca44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 步骤3：特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5dcb6a55-d4a2-4937-8708-ea64957f23c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征x有一些非数值的维度，比如sex  smoker，该如何量化呢\n",
    "# 可以采用One-Hot独热编码，可由多种工具实现\n",
    "# 将非数值特征按照类似直接编码的原则进行编码\n",
    "# 比如西南方向编码为1 0 0 0  因为有4个方向，东南可以为 0 1 0 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a1895cc-372d-42e3-8f44-86d55483e903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     bmi  children smoker      charges\n",
       "0   19  27.900         0    yes  16884.92400\n",
       "1   18  33.770         1     no   1725.55230\n",
       "2   28  33.000         3     no   4449.46200\n",
       "3   33  22.705         0     no  21984.47061\n",
       "4   32  28.880         0     no   3866.85520"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['region', 'sex'], axis=1) #对不重要维度进行降噪\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b04af37b-53b6-405b-9458-1b6e2cc50e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>under</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>over</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>over</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>under</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>under</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age    bmi children smoker      charges\n",
       "0   19  under       no    yes  16884.92400\n",
       "1   18   over      yes     no   1725.55230\n",
       "2   28   over      yes     no   4449.46200\n",
       "3   33  under       no     no  21984.47061\n",
       "4   32  under       no     no   3866.85520"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def greater(df, bmi, num_child):\n",
    "    df['bmi'] = 'over' if df['bmi'] >= bmi else 'under'\n",
    "    df['children'] = 'no' if df['children'] == num_child else 'yes'\n",
    "    return df\n",
    "\n",
    "data = data.apply(greater, axis=1, args=(30, 0)) #bmi是否大于30，孩子有无是影响y的重要特征，进行二值化处理，args是元组类型，进行函数传参\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "14d49b8d-7e7c-4bb7-a911-f9410aab544b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>charges</th>\n",
       "      <th>bmi_over</th>\n",
       "      <th>bmi_under</th>\n",
       "      <th>children_no</th>\n",
       "      <th>children_yes</th>\n",
       "      <th>smoker_no</th>\n",
       "      <th>smoker_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age      charges  bmi_over  bmi_under  children_no  children_yes  \\\n",
       "0   19  16884.92400     False       True         True         False   \n",
       "1   18   1725.55230      True      False        False          True   \n",
       "2   28   4449.46200      True      False        False          True   \n",
       "3   33  21984.47061     False       True         True         False   \n",
       "4   32   3866.85520     False       True         True         False   \n",
       "\n",
       "   smoker_no  smoker_yes  \n",
       "0      False        True  \n",
       "1       True       False  \n",
       "2       True       False  \n",
       "3       True       False  \n",
       "4       True       False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.get_dummies(data) #One-Hot独热编码\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3a5d0e4e-8ddc-4f26-bf52-2d6f8316484c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从数据集中分理出x和y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f88d386-4ffc-416d-bb71-51ab4e1fe57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi_over</th>\n",
       "      <th>bmi_under</th>\n",
       "      <th>children_no</th>\n",
       "      <th>children_yes</th>\n",
       "      <th>smoker_no</th>\n",
       "      <th>smoker_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  bmi_over  bmi_under  children_no  children_yes  smoker_no  smoker_yes\n",
       "0   19     False       True         True         False      False        True\n",
       "1   18      True      False        False          True       True       False\n",
       "2   28      True      False        False          True       True       False\n",
       "3   33     False       True         True         False       True       False\n",
       "4   32     False       True         True         False       True       False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=data.drop('charges',axis=1) # 按列提取x,到charges左边中止\n",
    "y=data['charges'] # charges就是y\n",
    "x.fillna(0,inplace=True) #若data数据中没有数据，则用0填充替换\n",
    "y.fillna(0,inplace=True)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90f2891d-e957-493e-b1f0-256bcd9dac5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split # 分割训练集和测试集，测试集占30%\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9614de26-860a-4240-a71d-b262a3e804c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler # 对训练集进行标准归一化\n",
    "scaler=StandardScaler(with_mean=True,with_std=True).fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6d245444-9ccb-4c90-b7d5-948d74f19ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.37478952,  0.96019334, -0.96019334, ...,  0.86386843,\n",
       "         0.50133512, -0.50133512],\n",
       "       [ 1.81634691,  0.96019334, -0.96019334, ...,  0.86386843,\n",
       "        -1.99467376,  1.99467376],\n",
       "       [-0.92261213, -1.04145692,  1.04145692, ...,  0.86386843,\n",
       "         0.50133512, -0.50133512],\n",
       "       ...,\n",
       "       [ 1.74426904, -1.04145692,  1.04145692, ..., -1.15758369,\n",
       "        -1.99467376,  1.99467376],\n",
       "       [-1.42715721, -1.04145692,  1.04145692, ..., -1.15758369,\n",
       "         0.50133512, -0.50133512],\n",
       "       [ 1.52803543, -1.04145692,  1.04145692, ...,  0.86386843,\n",
       "         0.50133512, -0.50133512]], shape=(402, 7))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled=scaler.transform(x_train) # 接收训练集的归一化结果\n",
    "x_test_scaled=scaler.transform(x_test) # 测试集用训练集的scaler进行归一化，即沿用训练集的mean和std\n",
    "x_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "15f29c2a-f36d-4e52-a960-78e4e210e8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures # 进行多项式升维\n",
    "poly_features=PolynomialFeatures(degree=2,include_bias=False)\n",
    "x_train_scaled=poly_features.fit_transform(x_train_scaled)\n",
    "x_test_scaled=poly_features.fit_transform(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae1f58a6-009b-415e-804c-05cbd08e5230",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b0e79762-b3b4-47e6-baef-b23b3a9f2cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 步骤4：模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c4a519f2-7c1f-4edc-8f53-40636895a8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5cfc885f-9901-43b1-9cea-5ba9c7521ee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg=LinearRegression()\n",
    "reg.fit(x_train_scaled,np.log1p(y_train))# 线性回归要求y服从正态分布\n",
    "y_predict_linear=reg.predict(x_test_scaled) # 预测测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a4bb4a1b-ee78-4ed6-8c40-2fd2ed842980",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ridge回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f8fd8cda-90ac-478b-aadb-36e90d51dd19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge(alpha=0.4)\n",
    "ridge.fit(x_train_scaled, np.log1p(y_train))\n",
    "y_predict_ridge = ridge.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e58b22ba-f1d1-4985-9ef4-c06fd26a2fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  GradientBoostingRegressor梯度提升回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b4b9d382-a766-4bbb-9508-b5c8190a3ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "booster = GradientBoostingRegressor()\n",
    "booster.fit(x_train_scaled, np.log1p(y_train))\n",
    "y_predict_boost = ridge.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12270f06-6f5f-452a-9bb4-066c5aeccfee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "161e70ca-2930-4297-9645-e07145f8c2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 步骤5：模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2277fd74-c547-4eb3-af5a-70fdde970d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6393c58a-c136-4d30-87f6-af4fc8ae76bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 线性回归评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dd4155f2-76cc-45f4-a9a9-aabdfc00dc47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.3604352058861612),\n",
       " np.float64(0.42455581143115834),\n",
       " np.float64(4285.554749674369),\n",
       " np.float64(5374.221224278025))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_rmse_train=np.sqrt(mean_squared_error(y_true=np.log1p(y_train),y_pred=reg.predict(x_train_scaled))) # y_train标准化后的根号下MSE\n",
    "log_rmse_test=np.sqrt(mean_squared_error(y_true=np.log1p(y_test),y_pred=y_predict_linear)) # y_test标准化后的根号下MSE\n",
    "rmse_train=np.sqrt(mean_squared_error(y_true=y_train,y_pred=np.exp(reg.predict(x_train_scaled)))) # 原始y_train的MSE\n",
    "rmse_test=np.sqrt(mean_squared_error(y_true=y_test,y_pred=np.exp(reg.predict(x_test_scaled)))) # 原始y_test的MSE\n",
    "log_rmse_train,log_rmse_test,rmse_train,rmse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "008adbcf-0fd2-4fc4-b014-66f8f35ddfc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ridge回归评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f82e4c52-a9fd-4bf1-a268-c6417c7e731d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.3604352781925688),\n",
       " np.float64(0.42454028915163694),\n",
       " np.float64(4285.562547002985),\n",
       " np.float64(5374.613643604692))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_rmse_train = np.sqrt(mean_squared_error(y_true=np.log1p(y_train), y_pred=ridge.predict(x_train_scaled)))\n",
    "log_rmse_test = np.sqrt(mean_squared_error(y_true=np.log1p(y_test), y_pred=y_predict_ridge))\n",
    "rmse_train = np.sqrt(mean_squared_error(y_true=y_train, y_pred=np.exp(ridge.predict(x_train_scaled))))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_true=y_test, y_pred=np.exp(ridge.predict(x_test_scaled))))\n",
    "log_rmse_train, log_rmse_test, rmse_train, rmse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0d51e8d3-fd36-4f65-8234-bbbcfe85899b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  GradientBoostingRegressor梯度提升回归评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "44193d9e-397e-4173-b152-ea9663273b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.3428319309782206),\n",
       " np.float64(0.42454028915163694),\n",
       " np.float64(3942.9703455566873),\n",
       " np.float64(5244.935120097133))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_rmse_train = np.sqrt(mean_squared_error(y_true=np.log1p(y_train), y_pred=booster.predict(x_train_scaled)))\n",
    "log_rmse_test = np.sqrt(mean_squared_error(y_true=np.log1p(y_test), y_pred=y_predict_boost))\n",
    "rmse_train = np.sqrt(mean_squared_error(y_true=y_train, y_pred=np.exp(booster.predict(x_train_scaled))))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_true=y_test, y_pred=np.exp(booster.predict(x_test_scaled))))\n",
    "log_rmse_train, log_rmse_test, rmse_train, rmse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c4aac864-40a5-4ec0-a89b-6a336ce1d55a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 评估结果\n",
    "# 普通线性回归，Ridge回归和非线性的梯度提升算法预测结果接近\n",
    "# 说明做好特征工程，线性模型也能达到非线性模型类似的结果\n",
    "# 误差仍在5000左右，可以做更好的特征工程，或选择更好的算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6033f3eb-0d80-4904-be53-ddbb23c3a4d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
